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A Two-stage Discrete Particle Swarm Optimization For The Problem Of Multiple Multi-level Redundancy Allocation In Series Systems

机译:串联系统多级多级冗余分配问题的两阶段离散粒子群优化

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摘要

Nowadays, the redundancy allocation problem (RAP) is increasingly becoming an important tool in the initial stages of or prior to planning, designing, and control of systems. The multiple multi-level redun-dancy allocation problem (MMRAP) is an extension of the traditional RAP such that all available items for redundancy (system, module and component) can be simultaneously chosen. In this paper, a novel particle swarm optimization algorithm (PSO) called the two-stage discrete PSO (2DPSO) is presented to solve MMRAP in series systems such that some subsystems or modules consist of different components in series. To the best of our knowledge, this is the first attempt to use a PSO to MMRAP. The proposed PSO used a totally new, very simple, effective and efficient mechanism to move to the next position without velocity. The result obtained by 2DPSO has been compared with those obtained by genetic algorithm (GA) and binary PSO (BPSO). Computational results show that the proposed 2DPSO is very competitive and performs well in the number of times it finds the best solutions, the average numbers of the earliest finding of the best solutions, and computation times.
机译:如今,冗余分配问题(RAP)在系统的规划,设计和控制的初始阶段或之前,正日益成为重要的工具。多层多级冗余分配问题(MMRAP)是传统RAP的扩展,因此可以同时选择用于冗余的所有可用项目(系统,模块和组件)。本文提出了一种新颖的粒子群优化算法(PSO),称为两阶段离散PSO(2DPSO),用于解决串联系统中的MMRAP,从而某些子系统或模块由串联的不同组件组成。据我们所知,这是对MMRAP使用PSO的首次尝试。拟议的PSO使用一种全新,非常简单,有效和高效的机制来无速度移动到下一个位置。将2DPSO获得的结果与遗传算法(GA)和二进制PSO(BPSO)获得的结果进行了比较。计算结果表明,所提出的2DPSO具有很好的竞争力,并且在找到最佳解决方案的次数,发现最佳解决方案最早的平均次数以及计算时间方面表现出色。

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